In order to prevent diseases and to maintain and improve health, it is necessary to grasp human health conditions (whether the body temperature, blood pressure, body fat, and so on, are in normal ranges, respectively). However, it is difficult to directly measure the health conditions, so in various kinds of health equipment, a model is built which serves to estimate human health conditions based on certain biological information obtained by measurements. For example, in an electronic thermometer, a model is built which estimates body temperature from a temperature measured at underarm. Also, in a sphygmomanometer, a model is built which estimates blood pressure from a pressure applied to an arm and a measured sound. In addition, in a body composition meter, a model is built which estimates body composition such as a body fat ratio, etc., from weight, height, age, sex, and body impedance.
Such kinds of models are generally expressed by estimate equations which take as variates a variety of kinds of feature parameters obtained from humans. At the time of building a model, (1) data are first measured and collected from a lot of subjects being tested, so that feature parameters are prepared. The feature parameters include measured values which are obtained by measurements, calculated values which are calculated from one or a plurality of measured values, attribute values of the subjects being tested such as sex, etc. Then, subsequently, (2) feature parameters used for building a model are selected from the feature parameters thus prepared, and (3) a model is built by using the feature parameters thus selected.
With respect to (2) the selection of a feature parameter and (3) the building of a model, a variety of techniques have been studied and proposed in the past. As existing techniques in relation to (2), there are, for example, a technique that eliminates feature parameters with high similarity in meaning between feature parameters, and high similarity in the way of dividing information by the feature parameters (see patent document 1), a technique that evaluates information content or volume by using average mutual information in a plurality of feature parameters (see patent document 2), a technique that evaluates the goodness of a combination of feature parameters by using an error of prediction (see patent document 3), and so on. In addition, as existing techniques in relation to (3), there are a linear model (a single regression model, a multiple regression model), a non-linear model (a neural network, an SVM (Support Vector Machine)), and so on.    Patent Document 1: Japanese patent application laid-open No. 11-126212    Patent Document 2: Japanese patent application laid-open No. 4-84277    Patent Document 3: Japanese patent application laid-open No. 9-81731